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Hierarchical approaches to Text-based Offense Classification.

Jay Choi1, David Kilmer2, Michael Mueller-Smith1

  • 1University of Michigan, Ann Arbor, MI, USA.

Science Advances
|March 3, 2023
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Summary

Researchers developed a new Uniform Crime Classification Standard (UCCS) and Text-based Offense Classification (TOC) tool to standardize crime data analysis. This machine learning approach improves offense classification accuracy from raw descriptions.

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Area of Science:

  • Criminology
  • Computer Science
  • Data Science

Background:

  • Administrative crime data requires classification for analysis.
  • Existing offense classification schemes lack comprehensiveness and mapping tools.
  • Standardizing crime data is crucial for accurate research and policy.

Purpose of the Study:

  • Introduce the Uniform Crime Classification Standard (UCCS) schema.
  • Present the Text-based Offense Classification (TOC) tool for automated classification.
  • Improve the accuracy and consistency of crime offense categorization.

Main Methods:

  • Developed the UCCS, a new offense classification schema.
  • Created the TOC tool, a machine learning algorithm using a hierarchical, multilayer perceptron framework.
  • Trained the TOC tool on 313,209 hand-coded offense descriptions from 24 states.

Main Results:

  • The UCCS schema aims to better reflect offense severity and improve type disambiguation.
  • The TOC tool translates raw offense descriptions into UCCS codes.
  • Model performance was assessed by testing variations in data processing and modeling approaches on recall, precision, and F1 scores.

Conclusions:

  • The UCCS schema and TOC tool provide a standardized method for classifying administrative crime data.
  • This approach addresses the lack of comprehensive standards and mapping tools in crime analysis.
  • The developed tools offer a robust solution for improving the consistency and accuracy of crime data classification.